# SCATTERPLOTS
# Install, then load ggplot.
library (ggplot2)
# Every ggplot has data + aes + geom.
# **First geom_point() plot of cars data:
ggplot(data=cars) +
aes(x=speed, y=dist) +
geom_point()
# Change to a linear model.
# **Plot with 2 or more geoms:
p <- ggplot(data=cars) +
aes(x=speed, y=dist)+
geom_point() +
geom_smooth(method="lm")
p
## `geom_smooth()` using formula 'y ~ x'
# Adjusting labels & theme.
p + labs(title="Stopping Distance (feet) vs. Speed (MPH) of Cars",
x=("speed (MPH)"),
y=("distance (feet)"),
subtitle=("R Built-in Data Set"),
caption=("dataset: 'cars'")) +
theme_bw()
## `geom_smooth()` using formula 'y ~ x'
# New data set...
# **Read an input file
url <- "https://bioboot.github.io/bimm143_S20/class-material/up_down_expression.txt"
genes <- read.delim(url)
head(genes)
## Gene Condition1 Condition2 State
## 1 A4GNT -3.6808610 -3.4401355 unchanging
## 2 AAAS 4.5479580 4.3864126 unchanging
## 3 AASDH 3.7190695 3.4787276 unchanging
## 4 AATF 5.0784720 5.0151916 unchanging
## 5 AATK 0.4711421 0.5598642 unchanging
## 6 AB015752.4 -3.6808610 -3.5921390 unchanging
# Q. How many genes are there in the data set?
nrow(genes)
## [1] 5196
# Q. What are the column names?
colnames(genes)
## [1] "Gene" "Condition1" "Condition2" "State"
# Q. How many columns are there?
ncol(genes)
## [1] 4
# Q. How many 'up' regulated genes are there?
table(genes$State)
##
## down unchanging up
## 72 4997 127
#Q. What fraction of total genes is up-regulated in this dataset? (2 sig figs)
prec <- table(genes$State) / nrow(genes) * 100
round (prec, 2)
##
## down unchanging up
## 1.39 96.17 2.44
# Q. Make plot
q <- ggplot(data=genes) +
aes(x=Condition1, y=Condition2, col=State) +
geom_point()
q
# **Plot with custom settings.
q + scale_color_manual(values=c("gold", "gray", "lightblue"))
# **Plot with labs settings.
q + scale_color_manual(values=c("gold", "gray", "lightblue")) +
labs (title="Gene Expression Changes Upon Drug Treatment",
x=("Control (no drug)"),
y=("Drug Treatment"))
# New data set...
# **Install, then load gapminder.
library (gapminder)
# Install, then load dplyr.
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
# Filter to year 2007.
gapminder_2007 <- gapminder %>% filter(year==2007)
# Q. Scatter plot of gapminder_2007.
ggplot(gapminder_2007) +
aes(x=gdpPercap, y=lifeExp, color=continent, size=pop) +
geom_point(alpha=0.5)
# Another version: points colored by popultion size.
ggplot(gapminder_2007) +
aes(x=gdpPercap, y=lifeExp, color=pop) +
geom_point(alpha=0.5)
# Another version: adjusting point size based on population size.
ggplot(gapminder_2007) +
geom_point(aes(x = gdpPercap, y = lifeExp,
size = pop), alpha=0.5) +
scale_size_area(max_size = 10)
# Q. For the year 1957:
gapminder_1957 <- gapminder %>% filter(year==1957)
ggplot(gapminder_1957) +
aes(x=gdpPercap, y=lifeExp,
color=continent,
size=pop)+
geom_point(alpha=0.7)+
scale_size_area(max_size=10)
# Q. For the years 1957 AND 2007"
gapminder_1957.2007 <- gapminder %>% filter(year==1957 | year==2007)
ggplot(gapminder_1957.2007) +
aes(x=gdpPercap, y=lifeExp,
color=continent,
size=pop)+
geom_point(alpha=0.7)+
scale_size_area(max_size=10) +
facet_wrap(~year)
# BAR CHARTS
# Data for 5 biggest countries:
gapminder_top5 <- gapminder %>%
filter(year==2007) %>%
arrange(desc(pop)) %>%
top_n(5, pop)
gapminder_top5
## # A tibble: 5 × 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 China Asia 2007 73.0 1318683096 4959.
## 2 India Asia 2007 64.7 1110396331 2452.
## 3 United States Americas 2007 78.2 301139947 42952.
## 4 Indonesia Asia 2007 70.6 223547000 3541.
## 5 Brazil Americas 2007 72.4 190010647 9066.
# A simple bar chart:
ggplot(gapminder_top5) +
geom_col(aes(x=country,y=pop))
# Fill by continent:
ggplot(gapminder_top5) +
geom_col(aes(x=country,y=pop,
fill=continent) )
# Fill by life expectancy:
ggplot(gapminder_top5) +
geom_col(aes(x=country,y=pop,
fill=lifeExp) )
# Fill by GDP per capita, change the order of bars:
ggplot(gapminder_top5) +
aes(x=reorder(country,-pop),y=pop, fill=gdpPercap)+
geom_col()
# Just fill by country
ggplot(gapminder_top5) +
aes(x=reorder(country, -pop), y=pop, fill=country) +
geom_col(col="gray30") +
guides(fill=FALSE)
## Warning: `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> =
## "none")` instead.
# New data set...
head(USArrests)
## Murder Assault UrbanPop Rape
## Alabama 13.2 236 58 21.2
## Alaska 10.0 263 48 44.5
## Arizona 8.1 294 80 31.0
## Arkansas 8.8 190 50 19.5
## California 9.0 276 91 40.6
## Colorado 7.9 204 78 38.7
# Flipped bar chart:
USArrests$State <- rownames(USArrests)
ggplot(USArrests) +
aes(x=reorder(State,Murder),y=Murder) +
geom_col() +
coord_flip()
# New format:
USArrests$State <- rownames(USArrests)
ggplot(USArrests) +
aes(x=reorder(State,Murder), y=Murder) +
geom_point() +
geom_segment(aes(x=State,
xend=State,
y=0,
yend=Murder), color="blue") +
coord_flip()
# PLOT ANIMATION
# Install, then load gifski & gganimate.
library (gganimate)
library (gifski)
# Normal ggplot of gapminder data:
ggplot (gapminder, aes(gdpPercap, lifeExp, size=pop, color=country))+
geom_point(alpha=0.7, show.legend=FALSE) +
scale_color_manual(values=country_colors) +
scale_size(range= c(2,12)) +
scale_x_log10()+
# Facet by continent
facet_wrap (~continent) +
# Animations :-)
labs(title = 'Year: {frame_time}', x = 'GDP per capita', y = 'life expectancy') +
transition_time(year) +
shadow_wake(wake_length = 0.1, alpha = FALSE)
# Combining plots
# Install, then load patchwork:
library(patchwork)
# Setup some example plots
p1 <- ggplot(mtcars) + geom_point(aes(mpg, disp))
p2 <- ggplot(mtcars) + geom_boxplot(aes(gear, disp, group = gear))
p3 <- ggplot(mtcars) + geom_smooth(aes(disp, qsec))
p4 <- ggplot(mtcars) + geom_bar(aes(carb))
# Use patchwork to combine them here:
(p1 | p2 | p3) /
p4
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
